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Chinese clinical named entity recognition (CNER) using pre-trained BERT model

Introduction

Code for paper Chinese clinical named entity recognition with variant neural structures based on BERT methods

Paper url: https://www.sciencedirect.com/science/article/pii/S1532046420300502

We pre-trained BERT model to improve the performance of Chinese CNER. Different layers such as Long Short-Term Memory (LSTM) and Conditional Random Field (CRF) were used to extract the text features and decode the predicted tags respectively. And we also proposed a new strategy to incorporate dictionary features into the model. Radical features of Chinese characters were also used to improve the model performance.

Model structure

Model Structure

Usage

Pre-trained models

For replication, we uploaded two models in Baidu Netdisk.

Link: https://pan.baidu.com/s/1obzG6OSbu77duhusWg2xmQ Code: k53q

Examples

To replicate the result of CCKS-2018 dataset

python main.py \
--data_dir=data/ccks_2018 \
--bert_model=model/  \
--output_dir=./output  \
--terminology_dicts_path="{'medicine':'data/ccks_2018/drug_dict.txt','surgery':'data/ccks_2018/surgery_dict.txt'}" \
--radical_dict_path data/radical_dict.txt \
--constant=0 \
--add_radical_or_not=True \
--radical_one_hot=False \
--radical_emb_dim=20 \
--max_seq_length=480 \
--do_train=True \
--do_eval=True \
--train_batch_size=6 \
--eval_batch_size=4 \
--hidden_dim=64 \
--learning_rate=5e-5 \
--num_train_epochs=5 \
--gpu_id=3 \

Results

CCKS-2018 dataset

Method P R F1
FT-BERT+BiLSTM+CRF 88.57 89.02 88.80
+dictionary 88.58 89.17 88.87
+radical(one-hot encoding) 88.51 89.39 88.95
+radical(random embedding) 89.24 89.11 89.17
+dictionary +radical 89.42 89.22 89.32
ensemble 89.59 89.54 89.56
Team Name Method F1
Yang and Huang (2018) CRF(feature-rich + rule) 89.26
heiheihahei LSTM-CRF(ensemble) 88.92
Luo et al.(2018) LSTM-CRF(ensemble) 88.63
dous12 - 88.37
chengachengcheng - 88.30
NUBT-IBDL - 87.62
Our FT-BERT+BiLSTM +CRF+Dictionary(ensemble) 89.56

CCKS-2017 dataset

Method P R F1
FT-BERT+BiLSTM+CRF 91.64 90.98 91.31
+dictionary 91.49 90.97 91.23
+radical(one-hot encoding) 91.83 90.80 91.35
+radical(random embedding) 92.07 90.77 91.42
+dictionary+radical 91.76 90.88 91.32
ensemble 92.06 91.15 91.60
Team Name Method F1
Qiu et al. (2018b) RD-CNN-CRF 91.32
Wang et al. (2019) BiLSTM-CRF+Dictionary 91.24
Hu et al. (2017) BiLSTM-FEA(ensemble) 91.03
Zhang et al. (2018) BiLSTM-CRF(mt+att+ms) 90.52
Xia and Wang (2017) BiLSTM-CRF(ensemble) 89.88
Ouyang et al. (2017) BiRNN-CRF 88.85
Li et al. (2017) BiLSTM-CRF(specialized +lexicons) 87.95
Our FT-BERT+BiLSTM +CRF+Dictionary(ensemble) 91.60

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